Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper, we describe concept parsing algorithms, a novel semantic analysis methodology at the core of a new pedagogy that focuses learners’ attention on deep comprehension of the conceptual content of learned material. Two new e‐learning tools are described in some detail: interactive concept discovery learning and meaning equivalence reusable learning objects. These semantic technologies were developed at the Ontario Institute for Studies in Education and Adaptive Technology Resource Centre of Faculty of Information Studies (ATRC/FIS) at the University of Toronto; they were tested since 2001 in several academic institutions in Canada and at the Russian Academy of Sciences (patents pending: US patent 6,953,344 B2, USPO ♯20050149510; Copyright 2005, PARCEP Inc.). We describe the rationale for developing these instructional tools, their main characteristics and results of several evaluative implementations that show their potential to enhance learning outcomes and to provide authentic, credible, evidence‐based formative assessments of learning processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it